Ultra-confidential self-training and video analytics system for uncommon objects

Abstract

Disclosed is a customized self-training machine learning system for video analytics for rare targets. The customized self-training machine learning system has a data processing module, a data annotation module with a labeling module, an automatic model training module configured to self-train the model based on the user's desire to detect rare targets, a model verification module with automatic error analysis and label approval to optimize the model, a model deployment module coupled with a user operation interface module, and a video analysis module based on the trained rare targets.

1 . A customized self-training machine learning system for video analytics for rare targets, comprising:
 a data processing module;   a data annotation module with a labelling module;   an automatic model training module configured to self-train a machine learning model based on the user's desire to detect rare targets;   a model verification module;   wherein the model verification module comprises an automatic error analysis and a label approval to optimize the machine learning model;   a model deployment module coupled with a user operation interface module; and   a video analysis module based on the trained rare targets.   
     
     
         2 . The customized self-training machine learning system for video analytics for rare targets, according to  claim 1 , wherein the data processing module comprises an automatic data cleaning module, a data verification module, and a data enhancement module. 
     
     
         3 . The customized self-training machine learning system for video analytics for rare targets, according to  claim 1 , wherein the automatic model training module comprises a unimodal, a cross-modal, and a multimodal. 
     
     
         4 . The customized self-training machine learning system for video analytics for rare targets, according to  claim 1 , wherein the automatic model training module comprises at least one built-in algorithm. 
     
     
         5 . The customized self-training machine learning system for video analytics for rare targets, according to  claim 1 , wherein the automatic model training module comprises meta-learning for detecting rare targets. 
     
     
         6 . The customized self-training machine learning system for video analytics for rare targets, according to  claim 1 , wherein the automatic model training module comprises a visualization module, an auto-tuning of hyperparameters module, a distributed training module and an automatic start or stop module. 
     
     
         7 . The customized self-training machine learning system for video analytics for rare targets, according to  claim 1 , wherein the rare targets comprise scarce, non-general and confidential objects such as medical or military imaging. 
     
     
         8 . The customized self-training machine learning system for video analytics for rare targets, according to  claim 1 , wherein the automatic model training module is configured to detect common targets and customized targets. 
     
     
         9 . The customized self-training machine learning system for video analytics for rare targets, according to  claim 1 , wherein the model deployment module comprises a data management module, a model packaging, a model management module, a labelling task management module, and a training task management module. 
     
     
         10 . The customized self-training machine learning system for video analytics for rare targets, according to  claim 9 , wherein the model management module is configured to provide model recommendations for users viewing specific information based on the generated model information. 
     
     
         11 . The customized self-training machine learning system for video analytics for rare targets, according to  claim 1 , wherein the video analysis module comprises a model management module, an analyzing module, an acceleration module, and an optimization module. 
     
     
         12 . The customized self-training machine learning system for video analytics for rare targets, according to  claim 11 , wherein the optimization module is configured to execute automatic error analysis and model automatic optimization of the model to optimize the video analysis performance. 
     
     
         13 . The customized self-training machine learning system for video analytics for rare targets, according to  claim 1 , wherein the system further comprises network-attached storage (NAS) or storage area network (SAN) providing secure and large-capacity data storage. 
     
     
         14 . A method of providing customized self-training machine learning models for video analytics for rare targets, comprising of:
 processing data;   automatically self-training a machine learning model using the processed data;   wherein using meta-learning in the automatic model training for detecting rare targets; and   analyzing a video based on the trained rare targets.   
     
     
         15 . The method of providing customized self-training of machine learning models for video analytics for rare targets, according to  claim 14 , wherein the processing data comprises:
 uploading data;   verifying and cleaning the uploaded data;   enhancing the data; and   labelling the data.   
     
     
         16 . The method of providing customized self-training of machine learning models for video analytics for rare targets, according to  claim 14 , wherein the step of self-training machine learning model comprises:
 displaying progress of current training, remaining time, and accuracy of current training;   auto-tuning of hyperparameters;   distributing training; and   automatically starting or stopping the training.   
     
     
         17 . The method of providing customized self-training of machine learning models for video analytics for rare targets, according to  claim 16 , wherein the step of self-training machine learning model further comprises:
 detecting common targets and customized targets.   
     
     
         18 . The method of providing customized self-training of machine learning models for video analytics for rare targets, according to  claim 17 , wherein the method further comprises:
 providing preset scenarios and their mature models for video analysis models; and   adopting the detection of common targets when the user's needs are not met during the self-training of the machine learning model.   
     
     
         19 . The method of providing customized self-training of machine learning models for video analytics for rare targets, according to  claim 17 , wherein the method further comprises:
 using transfer learning; and   freezing a pre-determined number of network layers for detecting customized targets with a large amount of training data.   
     
     
         20 . The method of providing customized self-training of machine learning models for video analytics for rare targets, according to  claim 14 , wherein the method further comprises verifying the trained model to optimize the machine learning model after the training is completed. 
     
     
         21 . The method of providing customized self-training of machine learning models for video analytics for rare targets, according to  claim 20 , wherein the method further comprises:
 performing data management, model management and labeling management using a user operation interface; and   performing training task management using the user operation interface after verifying the trained model.   
     
     
         22 . The method of providing customized self-training of machine learning models for video analytics for rare targets, according to  claim 14 , wherein the analyzing of video comprises:
 providing model recommendations and automatic deployment;   providing at least one image analysis, video analysis and batch analysis;   accelerating the machine learning model; and   activating optimization module to realize automatic error analysis and model automatic optimization of the machine learning model to optimize video analysis performance.   
     
     
         23 . The method of providing customized self-training of machine learning models for video analytics for rare targets, according to  claim 22 , wherein the method further comprises simultaneously updating output feedback to the machine learning models.